from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-22 14:12:43.458447
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sun, 22, Nov, 2020
Time: 14:12:47
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.4650
Nobs: 118.000 HQIC: -43.7202
Log likelihood: 1213.20 FPE: 4.38055e-20
AIC: -44.5782 Det(Omega_mle): 2.10658e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.707471 0.213958 3.307 0.001
L1.Burgenland 0.137485 0.092502 1.486 0.137
L1.Kärnten -0.307225 0.077489 -3.965 0.000
L1.Niederösterreich -0.009277 0.224626 -0.041 0.967
L1.Oberösterreich 0.268902 0.181638 1.480 0.139
L1.Salzburg 0.128160 0.091484 1.401 0.161
L1.Steiermark 0.088049 0.129542 0.680 0.497
L1.Tirol 0.162060 0.085487 1.896 0.058
L1.Vorarlberg 0.015784 0.084850 0.186 0.852
L1.Wien -0.156794 0.175212 -0.895 0.371
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.748246 0.273233 2.738 0.006
L1.Burgenland -0.022731 0.118128 -0.192 0.847
L1.Kärnten 0.347233 0.098957 3.509 0.000
L1.Niederösterreich 0.063158 0.286856 0.220 0.826
L1.Oberösterreich -0.213396 0.231960 -0.920 0.358
L1.Salzburg 0.165318 0.116829 1.415 0.157
L1.Steiermark 0.189494 0.165430 1.145 0.252
L1.Tirol 0.137242 0.109171 1.257 0.209
L1.Vorarlberg 0.191335 0.108357 1.766 0.077
L1.Wien -0.573217 0.223753 -2.562 0.010
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.369804 0.090468 4.088 0.000
L1.Burgenland 0.100714 0.039113 2.575 0.010
L1.Kärnten -0.027063 0.032765 -0.826 0.409
L1.Niederösterreich 0.125429 0.094979 1.321 0.187
L1.Oberösterreich 0.264031 0.076802 3.438 0.001
L1.Salzburg -0.003823 0.038682 -0.099 0.921
L1.Steiermark -0.064675 0.054774 -1.181 0.238
L1.Tirol 0.098435 0.036147 2.723 0.006
L1.Vorarlberg 0.143105 0.035877 3.989 0.000
L1.Wien 0.005257 0.074085 0.071 0.943
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.220284 0.108409 2.032 0.042
L1.Burgenland 0.001634 0.046869 0.035 0.972
L1.Kärnten 0.035184 0.039263 0.896 0.370
L1.Niederösterreich 0.088421 0.113814 0.777 0.437
L1.Oberösterreich 0.348026 0.092033 3.782 0.000
L1.Salzburg 0.091637 0.046354 1.977 0.048
L1.Steiermark 0.194018 0.065637 2.956 0.003
L1.Tirol 0.028424 0.043315 0.656 0.512
L1.Vorarlberg 0.110318 0.042992 2.566 0.010
L1.Wien -0.117321 0.088777 -1.322 0.186
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.909945 0.232580 3.912 0.000
L1.Burgenland 0.032753 0.100553 0.326 0.745
L1.Kärnten -0.014751 0.084234 -0.175 0.861
L1.Niederösterreich -0.139430 0.244176 -0.571 0.568
L1.Oberösterreich 0.046381 0.197447 0.235 0.814
L1.Salzburg 0.056423 0.099447 0.567 0.570
L1.Steiermark 0.110954 0.140817 0.788 0.431
L1.Tirol 0.238295 0.092928 2.564 0.010
L1.Vorarlberg 0.024233 0.092235 0.263 0.793
L1.Wien -0.229009 0.190462 -1.202 0.229
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.193186 0.161767 1.194 0.232
L1.Burgenland -0.041770 0.069937 -0.597 0.550
L1.Kärnten -0.010337 0.058587 -0.176 0.860
L1.Niederösterreich 0.204538 0.169832 1.204 0.228
L1.Oberösterreich 0.395125 0.137331 2.877 0.004
L1.Salzburg -0.036963 0.069168 -0.534 0.593
L1.Steiermark -0.055748 0.097942 -0.569 0.569
L1.Tirol 0.196546 0.064634 3.041 0.002
L1.Vorarlberg 0.054334 0.064152 0.847 0.397
L1.Wien 0.112045 0.132472 0.846 0.398
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.350629 0.205562 1.706 0.088
L1.Burgenland 0.060146 0.088871 0.677 0.499
L1.Kärnten -0.080389 0.074448 -1.080 0.280
L1.Niederösterreich -0.150159 0.215811 -0.696 0.487
L1.Oberösterreich -0.121974 0.174510 -0.699 0.485
L1.Salzburg -0.001739 0.087894 -0.020 0.984
L1.Steiermark 0.381513 0.124458 3.065 0.002
L1.Tirol 0.538559 0.082132 6.557 0.000
L1.Vorarlberg 0.223456 0.081520 2.741 0.006
L1.Wien -0.187448 0.168336 -1.114 0.265
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.218913 0.235904 0.928 0.353
L1.Burgenland 0.007201 0.101989 0.071 0.944
L1.Kärnten -0.070030 0.085437 -0.820 0.412
L1.Niederösterreich 0.206488 0.247665 0.834 0.404
L1.Oberösterreich 0.011244 0.200269 0.056 0.955
L1.Salzburg 0.233426 0.100868 2.314 0.021
L1.Steiermark 0.157437 0.142829 1.102 0.270
L1.Tirol 0.054615 0.094256 0.579 0.562
L1.Vorarlberg -0.000780 0.093553 -0.008 0.993
L1.Wien 0.199326 0.193184 1.032 0.302
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.680656 0.131436 5.179 0.000
L1.Burgenland -0.010291 0.056825 -0.181 0.856
L1.Kärnten -0.009323 0.047602 -0.196 0.845
L1.Niederösterreich -0.084950 0.137989 -0.616 0.538
L1.Oberösterreich 0.265644 0.111582 2.381 0.017
L1.Salzburg 0.002802 0.056200 0.050 0.960
L1.Steiermark 0.011302 0.079579 0.142 0.887
L1.Tirol 0.079101 0.052515 1.506 0.132
L1.Vorarlberg 0.184135 0.052124 3.533 0.000
L1.Wien -0.106185 0.107634 -0.987 0.324
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.079520 -0.076726 0.191353 0.237270 0.015205 0.066719 -0.144714 0.098302
Kärnten 0.079520 1.000000 -0.081605 0.169405 0.055357 -0.165140 0.167956 0.002832 0.269115
Niederösterreich -0.076726 -0.081605 1.000000 0.216274 0.025793 0.144279 0.057586 0.028477 0.348643
Oberösterreich 0.191353 0.169405 0.216274 1.000000 0.230192 0.261324 0.062423 0.048351 0.027977
Salzburg 0.237270 0.055357 0.025793 0.230192 1.000000 0.134916 0.029719 0.056479 -0.084167
Steiermark 0.015205 -0.165140 0.144279 0.261324 0.134916 1.000000 0.094715 0.094128 -0.205140
Tirol 0.066719 0.167956 0.057586 0.062423 0.029719 0.094715 1.000000 0.126281 0.079417
Vorarlberg -0.144714 0.002832 0.028477 0.048351 0.056479 0.094128 0.126281 1.000000 0.062348
Wien 0.098302 0.269115 0.348643 0.027977 -0.084167 -0.205140 0.079417 0.062348 1.000000